Regional Energy–Economy–Environment Coupling Coordinated Development System Driven by Carbon Peaking and Carbon Neutralization over 13 Cities in Jiangsu Province

Abstract

It is important to explore the energy–economy–environment (3E) coupling mechanism for building a sustainable economy in the context of carbon-peaking and carbon-neutralization strategy. Based on the DPSIRM (driving force–pressure–state–influence–response–management) theoretical model, this paper constructs the evaluation system of 3E coupling-coordinated development, takes the carbon-neutral and sustainable-development strategy and spatiotemporal heterogeneity into account in the index system, and constitutes the energy–economy–environment and carbon-neutral strategy–sustainable development–space (3E3S) strategic-development model. This paper uses the coupling-coordination-degree model to measure the coordinated development of 3E in Jiangsu from 2010 to 2020 and analyzes the time-sequence evolution and spatial-difference evolution characteristics of the coupling-coordinated development. The results show that (1) when the coupling coefficient of 3E was in a stable and high-level coupling state and the correlation degree of each system was high, the coupling-coordination degree increased from 0.4 in 2010 to 0.7 in 2020; the level of coordination of 3E coupling transited from the initial maladjustment recession to the intermediate coordinated development and moved towards high-quality coordinated development. (2) In addition, at the beginning, the development level of southern and central Jiangsu was generally higher than that of northern Jiangsu; in the middle term, the overall development was in a stage of barely coordinated development. The urban difference between northern and central Jiangsu was relatively high, and the level in southern Jiangsu was generally high; in the later stage, the overall development was in a well-coordinated stage. The development of southern Jiangsu was relatively saturated, gradually approaching the stage of high-quality coordinated development. Roughly speaking, in 2010, the average level of coupling coordination degree of South Jiangsu, Central Jiangsu, and North Jiangsu was 0.5, 0.4, and 0.3, respectively; by 2020, it had developed to 0.9, 0.8, and 0.7, respectively, and the development level rose steadily despite regional differences. The level of coupling coordination generally increased from north to south. Therefore, strengthening the strategic-development awareness of 3E and establishing and improving the government consultation mechanism according to local conditions will help decision-makers to formulate effective regional sustainable-development and carbon-neutral strategies and form a coordinated-development strategy of 3E3S in Jiangsu.

1. Introduction

It is a difficult problem for all countries in the world to solve the problem of moving towards carbon neutrality under sustainable development and achieving economic growth and a better environment. The rapid increase in carbon emissions has caused great pressure on the global ecological environment and accelerated the trend of global warming [1,2]. At the same time, due to global warming, increasing extreme weather, and expanding human activities, problems such as deforestation, grassland degradation, desertification, water and soil loss, and other problems are becoming increasingly serious [3]. Now that China is in a period of rapid economic development, the contradiction between the huge demand for energy for economic development and the scarcity of energy is prominent. The “3E” (economy–energy–environment) system coupling and coordinated development of energy, economy, and environment provides a basis for ecological environment protection and high-quality economic development [4,5,6]. In order to achieve more rational, scientific, and sustainable development, the Chinese government proposed in 2020 to achieve peak carbon emissions by 2030 and implement a carbon-neutral development strategy by 2060.

Jiangsu’s GDP has been at the forefront of the country for a long time, and it is a strong economic province. However, although it is pursuing economic development excessively, there are some problems with environmental protection and resource utilization. The energy structure of Jiangsu is highly dependent on coal, with relatively high carbon emissions and a heavy task of energy conservation and emissions reduction. In 2021, the GDP of Jiangsu was CNY 11,636.42 billion, an increase of 8.6% over the previous year, ranking the second in the country. At the same time, due to the great difference in the economic-development level of each region in Jiangsu, the environmental status and resource utilization are different, and there is a certain regional heterogeneity. Therefore, this paper chooses Jiangsu Province as the research object; explores the level of coordinated development of the coupling of economy, energy, and environment at the urban level; analyzes the temporal and spatial differences of urban coupling coordination; and provides a demonstration reference for China to achieve sustainable development and carbon-neutral strategic goals.

When building the energy–economy–environment coupling-coordination indicator system, this paper comprehensively considers the role of sustainable-development strategy and carbon-neutral strategy, analyzes the spatial and temporal heterogeneity of Jiangsu urban agglomeration, and establishes a 3E3S strategic model. Figure 1 shows the technical roadmap for the specific implementation of the 3E3S strategic model. Figure 1 shows the technical roadmap for the specific implementation of the 3E3S strategic model.

Coupling is a classical concept in the field of physics, focusing on the interaction of two or more independent units, which leads to the energy-exchange process with matter as the carrier [7]. The coupling theory combines two or more systems into a large and complex system and measures the coordination between systems by analyzing their coherence and coupling [8,9]. In 1995, American scholars Grossman and Krueger proposed the famous Environmental Kuznets Curve (EKC), which reflects the “inverted U” relationship between environmental quality and economic development under ideal conditions [10]. Based on the coupling theory, a coupling-coordination model was established to test the coordination between economic growth and ecological environmental health, such as resource utilization, energy consumption, and air pollution [11,12,13].

For the energy–economy relationship, relevant scholars have found that economic growth can improve energy efficiency and promote the promotion and application of energy-saving technologies [14,15]. He and Liu [16,17] analyzed the relationship between urbanization and ecological environment. Shen [18] discussed the coordination between social economy and carbon emissions. Song [13] focused on the relationship between carbon emissions and urbanization.

Li discussed the spatial-distribution characteristics [19], dynamic-evolution laws, and spatial-agglomeration effects of the coupling and coordinated development of China’s provincial economic, energy, and environmental systems on the basis of qualitative analysis of the mechanism of the coupling and coordinated evolution of economic, energy, and environmental systems. The interrelationship of each subsystem of the 3E system has been analyzed, such as studying the dynamic relationship between energy consumption and economic growth, energy consumption, and environmental pollution [20,21]. The sustainable development of the 3E system has also been studied, for example, by analyzing the coupling and coordinated development of the 3E system in China, Australia, the “the Belt and Road,” and other countries [22]. Analysis of the 3E system and external impacts, including impacts on energy structure, carbon-emissions quotas, technological innovation, etc., has also been carried out [23,24,25].

As for the indicators for measuring the degree of regional coordinated development, some scholars emphasized the level of regional development, and the measurement results are shown as the degree of regional development [26]. Some scholars placed particular emphasis on the regional coordination level, studied the measurement of regional coordination indicators, and proposed a regional coordination model, such as a dispersion-coefficient coordination model [27], a Euclidean-distance coordination-development-degree clustering model [28]. The indicator-measurement methods used are also different. Common methods include principal component analysis, factor analysis, analytic hierarchy process, directional distance function DEA [29].

According to the research on the relationship between energy, economy, and environment in Jiangsu, Xue [30] used the DEA method to calculate that Jiangsu Province, as a major energy-importing province in China, has good economic performance but poor environmental performance, low energy-consumption efficiency, and a downward trend from 2000 to 2010. Shen [31] believed that Jiangsu is facing problems that affect the coordinated development of the environment, society, and economy, such as weak environmental carrying capacity, biased economic structure, unreasonable energy consumption, and unbalanced regional development. Yang [32] discussed the synergy of the four systems of energy, economy, environment, and ecology in Hubei Province and measured the coupling coordination of the two systems and the 4E integrated system using the coupling-coordination model.

On the basis of the DPSIR theoretical model, this paper adds the feedback function of the management M subsystem; considers the governance role of the government in energy, economy, and environment; and constructs an indicator system to innovate and optimize the existing model. The OECD proposed for the first time that in the face of a large number of new environmental data, it is necessary to establish an indicator that can reflect environmental trends [33]. Based on this, in the early 1980s, scholars put forward the pressure–state–response (PSR) analysis framework on the basis of the relationship between environmental pressure, status quo, and response explained by the OECD. Environmental indicators not only describe the trend of environmental change, but also enable decision-makers to focus on the impact of human activities on the environment [34,35]. Subsequently, the pressure–state–response potential (PSRP) framework of the United Nations Commission on Sustainable Development (UNCSD) improved and revised the original framework, further reflecting the relationship between socio-economics and the resource environment [36]. In 1993, the OECD first put forward the concept model of driving force–pressure–state–influence–response (DPSIR) [37] based on the basic model and previous research results. The DPSIRM model is an innovative model proposed by Chinese scholars that incorporates a management (M) subsystem into the DPSIR model. It was first applied by Yang Jun et al. to the study of human settlement and the ecological environment, and refined the feedback of management on ecological impact at the standard level [38].

The innovation of the DPSIRM theoretical framework in this paper adds the feedback function of the management subsystem. This model is integrated with the ecological-management measures of government departments to build a system framework that is highly objective and scientific. Comprehensive assessment of the relationship between economy, energy, and environment is also widely used in the field of living-environment management and sustainable development, as well as in other regions. The specific action process is shown in Figure 2.

Through the above literature analysis, we found that the existing research has the following deficiencies:

Most of the existing studies focused more on the national and provincial perspectives and less on the micro level of urban agglomeration. In the construction of the index system of energy, economy, and environment, the government’s macro-control of the 3E system was ignored. Most of the existing literature measured the coupling-coordination degree and spatio-temporal change of the 3E system, but did not quantitatively analyze the impact of each subsystem on coupling-coordination scheduling. In order to make up for the shortage of previous literature, the following improvements have been made in this paper:

Based on the micro perspective of the city level, taking the dual perspectives of time and space as the starting point, this paper investigates the coupling and coordination of energy, economy, and environment of cities in Jiangsu Province; analyzes the overall level of coordinated development; comes to an understanding of the changing trend and macro trend of the micro urban 3E system; and makes up for the shortcomings of existing research limited to the national and provincial perspectives. From the perspective of government management, the construction of indicators in the three dimensions of energy, economy, and environment are considered. From the micro perspective of refining subsystems, it is a new perspective in this research field to analyze the coupling relationship within the six subsystems, accurately describe the complexity of the system and the causal relationship between various factors, comprehensively reflect the internal relationship between systems, and fill in the research gap of the literature related to the 3E coupling-coordination model from the perspective of macro regulation. In addition, this paper innovatively takes the carbon-neutral strategy and sustainable-development strategy into account; integrates them with the energy–economy–environment coupling system and takes into account the regional spatial and temporal heterogeneity characteristics; constructs a regional synergy and integration system of southern, central, and northern Jiangsu and combs the interaction and feedback relationship between different regions; establishes a 3E3S strategic model; and strives to promote the realization of collaboration, sharing, and integration in the “double carbon” process. Figure 3 shows the framework of this paper.

The main innovation points of this paper are as follows:

(1) Based on DPSIR theory, the management element is introduced, and indicators of energy, economy, and environment are constructed from the perspective of government management. From the microscopic perspective of subsystem refinement, the coupling relationship within the six subsystems is analyzed, the system complexity and the causal relationship between various factors are described, and the internal relationship between systems is comprehensively reflected.

(2) Before building the energy–economy–environment coupling scheduling model, priority should be given to the energy–economy, energy–environment, and economy–environment binary systems. As the basic unit of the coupling system, the influence coefficient of the binary system will directly affect the ternary-coupling coordination system. Considering the influence coefficient of the binary system, the coupling and coordination relationship between the ternary systems can be analyzed more clearly and intuitively.

(3) Based on the micro perspective and the urban level, the coupling and coordination of energy–economy–environment in 13 cities of Jiangsu are investigated from the perspective of time and space, the overall coordinated-development level is analyzed, and the future trend is predicted.

(4) This paper is based on the urban level on the basis of the existing energy, economic, and environmental systems, taking into account the carbon-neutral strategy and sustainable-development strategy, combined with the characteristics of spatial and temporal heterogeneity of Jiangsu, to build a 3E3S integration-mechanism strategic model.

2. Materials and Methods

2.1. Index-System Construction

Based on the theoretical framework of DPSIRM, this paper constructed a 3E indicator system through six aspects: driving force, pressure, state, impact, response, and management. Based on the 3E comprehensive evaluation index of Jiangsu, this paper analyzed the internal coupling relationship of the six subsystems of the DPSIRM model. Based on the urban level, considering the coupling and coordination relationship of the three subsystems, according to the actual development of Jiangsu, 25 indicators that can reflect 3E characteristics of cities at all levels were selected to evaluate the 3E coupling and coordination system in Jiangsu. The contents are shown in

Table 1. Economy–energy–environment indicator system.

Dimension	Theory	Indicator	Unit	Attribute
Economy	Driving force	AGDP	CNY	+
		Urban population ratio	%	+
	State	Per capita income	CNY	+
		Increase rate of tertiary industry	%	+
	Impact	PCIER	%	+
		GNI	CNY	+
		PCE	CNY	+
		Social labor productivity	%	+
	Response	R&D	Million CNY	+
Energy	Pressure	Energy consumption/GDP	100 tons/million y	−
		ECPC	tons/people	−
	State	Industrial power consumption	100 million kWh	−
		Social electricity consumption	100 million kWh	−
		Energy-consumption growth	%	−
	Impact	Energy-consumption elasticity	%	−
Environment	Pressure	Industrial SO2 emissions	Tons	+
		Industrial wastewater discharge	10,000 tons	+
		Industrial dust emissions	Tons	+
	State	Urban precipitation	Millimeters	−
		Total water resources	Billion m3	−
	Response	Utilization rate of industrial solid waste	%	−
		Urban sewage-treatment rate	%	+
		Harmless disposal of garbage	%	+
	Management	Environmental-protection expenditure	Million CNY	+
		Greening coverage	%	+

. Among them, the” +” is a positive indicator, and the “−” is a negative indicator.

The economic dimension mainly considers the driving force, status, impact, and response. The energy dimension mainly considers pressure, status, and impact. The environmental dimension mainly considers pressure, status, response, and management.

Under the requirements of sustainable development in 3E, it is crucial to highlight the position of the “double carbon” target in the evaluation-index system. Based on the construction of the 3E indicator system, this paper integrates the sustainable-development strategy and carbon-neutral strategy and constructs the 3E3S indicator-evaluation system and strategic model of cities in Jiangsu.

Table 2. Convergence metrics for sustainable development and 3E coupled systems.

	Sustainable-Development Indicators	3E Coupling-System Index
Economy	Scale Structure Benefit Ability	AGDP
		Urban population ratio
		Per capita income
		Increase rate of tertiary industry
		PCIER
		Social labor productivity
		R&D
Resource	Energy	Energy consumption/GDP
	Mineral products	ECPC
	Water	Industrial power consumption
	Land	Social electricity consumption
	Forest	Energy-consumption growth
	Ocean	Energy-consumption elasticity
Environment	Water Atmosphere Waste material Noise Land Natural disaster Biodiversity	Urban precipitation
		Total water resources
		Industrial SO2 emissions
		Industrial wastewater discharge
		Industrial dust emissions
		Utilization rate of industrial solid waste
		Urban sewage-treatment rate
		Harmless disposal of garbage
		Environmental-protection expenditure
		Greening coverage

and

Table 3. Convergence metrics of carbon neutralization strategies with 3E coupled systems.

Carbon-Neutral Strategy		3E Coupling-System Evaluation
Energy-structure adjustment	Improving energy use	Energy consumption/GDP
		ECPC
		Energy-consumption growth
	Reducing fossil-energy use	Social electricity consumption
		Urban precipitation
	Increasing clean-energy use	Environmental-protection expenditure
		Urban sewage-treatment rate
		Harmless disposal of garbage
		Greening coverage
Emissions reduction in key areas	Manufacturing industry Construction business	Industrial power consumption
		Industrial SO2 emissions
		Industrial wastewater discharge
		Industrial dust emissions
		Utilization rate of industrial solid waste
	Agriculture
	Transportation
Financial-abatement support		-

describe the integration principle and process of the energy–economy–environment 3E system indicators in detail, the carbon neutral strategy, and the sustainable-development strategy.

2.2. Construction of Coupling-Coordination Model

(1) Indicator standardization

Standardize the indicators, mainly using the mechanism-standardization method:

When $X_{ij}^{\prime }$ is a positive indicator, the expression is:

$$X_{ij}^{\prime }=\frac{X_{ij}-\min X_{ij}}{\max X_{ij}-\min X_{ij}}$$

(1)

When $X_{ij}^{\prime }$ is a negative indicator, the expression is:

$$X_{ij}^{\prime }=\frac{\max X_{ij}-X_{ij}}{\max X_{ij}-\min X_{ij}}$$

(2)

$X_{ij}$ is the variable value of index j of system i; $\max (X_{ij})$ and $\min (X_{ij})$ are the maximum and minimum values of index j of system I, respectively; and $X_{ij}^{\prime }$ is the standardized value of the indicator. The value range is [0, 1]. The larger the value, the more satisfied it is.

(2) Index-weight calculation

Different indicators have different impacts on the system. In order to eliminate the deviation and make the index weights of each system conform to the objective facts, this paper used the entropy method to calculate the index weights of the energy–economy–environment index-evaluation system. The specific steps are as follows:

(1)

Calculate the index proportion:

$$P_{ij}=X_{ij}^{\prime }/{\displaystyle \sum _{i=1}^n{X}_{ij}^{\prime }}$$

(3)

(2)

Calculate the index-entropy value:

$$E_j=-k{\displaystyle \sum _{i=1}^n{P}_{ij}\ln P_{ij}}$$

(4)

(3)

Calculate the index-entropy redundancy:

$$D_j=1-E_j$$

(5)

(4)

Weight-calculation results:

$$W_j=D_j/{\displaystyle \sum _{j=1}^m{D}_j}$$

(6)

(3) Calculation of comprehensive evaluation index

Based on the index-evaluation system of the energy–economy–environment system, a comprehensive evaluation function was constructed. In this paper, the method of the weighted sum of the weight and index was used to calculate the comprehensive evaluation index:

$$E_j={\displaystyle \sum _{j=1}^m{W}_j\times X_{ij}^{\prime }}$$

(7)

(4) Energy–economy–environment coupling-coordination model

(1)

Comprehensive evaluation model of the development level of the subsystem:

$$\{\begin{array}{l}{E}_{1i}={\displaystyle {\sum }_{i=1}^n{\alpha }_i{x}_i}\\ E_{2i}={\displaystyle {\sum }_{i=1}^n{\beta }_i{y}_i}\\ E_{3i}={\displaystyle {\sum }_{i=1}^n{\gamma }_i{z}_i}\end{array}$$

(8)

wherein $E_{1i}$, $E_{2i}$, and $E_{3i}$ represent the comprehensive evaluation indexes of the energy, economy, and environment systems, respectively, which are in direct proportion to the development level. ${\alpha }_i$, ${\beta }_i$, and ${\gamma }_i$ are the index weight of each subsystem, and $x_i$, $y_i$, and $z_i$ are the standardized values of each indicator.

(2)

Energy–economy–environment coupling degree

$$C_i={\left\{\frac{E_{1i}\times E_{2i}\times E_{3i}}{{\left[\left(E_{1i}+E_{2i}+E_{3i}\right)/3\right]}^3}\right\}}^{\frac{1}{3}}$$

(9)

wherein $C_i$ refers to the coupling degree. The value range is [0, 1]. The larger the value of C is, the greater the correlation between subsystems is and the closer the development is. When C = 0, it indicates that there is no relationship between the internal elements of the system and the system development state is disordered. When C = 1, it indicates that the three systems are in the optimal coupling state. The coupling degree refers to the degree of interaction between two parties, regardless of advantages and disadvantages. It is a measure of the degree of correlation between systems. It only reflects the degree of interaction between systems, not the level of each system.

(3)

Energy–economy–environment coordination

$$T_i=\alpha E_{1i}+\beta E_{2i}+\gamma E_{3i}$$

(10)

$T_i$ is the coordination degree of the energy–economy–environment system, and $\alpha$, $\beta$, and $\gamma$ are undetermined coefficients. With reference to existing research, the coefficient is generally recognized as 1/3. The degree of coordination refers to the degree of benign coupling in the interaction, which reflects the quality of coordination and indicates whether the functions promote each other at a high level or restrict each other at a low level. It can not only reflect whether each system has a good level, but also reflects the interaction between systems.

(4)

Energy–economy–environment coupling-coordination degree

$$D_i=\sqrt{C_i\times T_i}$$

(11)

$D_i$ refers to system coupling and co-scheduling, and the value range is [0, 1]. Generally, the coupling-coordination degree is divided into three types, namely, maladjustment-recession area, transition, and harmonious-development area [39]. The closer the value is to 1, the better the coupling coordination between systems is. This paper refers to Liao’s division standard [40] and divides the coupling-coordination types into 10 types, as shown in

Table 4. Degree and type of coupling and coordination.

Coupling-Coordination Degree D(t)	Partition Threshold D(t)	Types of Coupling Coordination
Dysregulation-regression area	[0, 0.1)	Extreme disorder decline
	[0.1, 0.2)	Dysregulated decline
	[0.2, 0.3)	Moderate disorder decline
	[0.3, 0.4)	Mild disorder decline
Transition-reconciliation area	[0.4, 0.5)	Moribund decline
	[0.5, 0.6)	Barely coordinated development
Coordinated-development areas	[0.6, 0.7)	Primary coordination development
	[0.7, 0.8)	Intermediate-level coordinated development
	[0.8, 0.9)	Well-coordinated development
	[0.9, 1)	Quality coordinated development

.

3. Results and Discussion

3.1. Descriptive Statistics

This paper divided Jiangsu into Southern Jiangsu (Nanjing, Central Jiangsu, Wuxi, Changzhou, and Zhenjiang), Central Jiangsu (Yangzhou, Taizhou, and Nantong), and Northern Jiangsu (Huai’an, Xuzhou, Lianyungang, Suqian, and Yancheng) according to the division of administrative regions (see Figure 4 for details). The main data sources of the article were the Jiangsu Statistical Yearbook, the China Urban Statistical Yearbook, the Intellectual Property Office, and the Jiangsu Provincial Department of Ecological Environment; MATLAB2021, Origin2021, and ArcGIS10.8 were mainly used for data calculation and analysis.

In this paper, the entropy value-weighting method was used to analyze the degree of correlation between order parameters and the amount of information provided to determine the weight of each index and then calculate the total effectiveness of the subsystem development.

Table 5. Economic–energy–environmental indicator weights.

Dimension	Theory	Indicator	Unit
Economy	Driving force	AGDP	0.0841
		Urban population ratio	0.0979
	State	Per capita income	0.0817
		Increase rate of tertiary industry	0.0858
	Impact	PCIER	0.0766
		GNI	0.1204
		PCE	0.0862
		Social labor productivity	0.2790
	Response	R&D	0.0883
Energy	Pressure	Energy consumption/GDP	0.1161
		ECPC	0.1948
	State	Industrial power consumption	0.1681
		Social electricity consumption	0.1828
		Energy-consumption growth	0.1137
	Impact	Energy-consumption elasticity	0.2245
Environment	Pressure	Industrial SO2 emissions	0.0838
		Industrial wastewater discharge	0.1161
		Industrial dust emissions	0.1464
	State	Urban precipitation	0.1037
		Total water resources	0.1025
	Response	Utilization rate of industrial solid waste	0.0616
		Urban sewage-treatment rate	0.1060
		Harmless disposal of garbage	0.0810
	Management	Environmental-protection expenditure	0.0768
		Greening coverage	0.1223

reports the weight values of 25 indicators calculated by the entropy method.

3.2. Temporal Evolution of Jiangsu 3E Coupling and Coordinated Development

(1)

Temporal evolution of binary-interaction system

The dual system is the most basic unit in the coupling system. This paper first studied the dual system, then extended it to the coordinated development of the multiple system coupling, and then further explored the quality level of the coordinated development of the energy–economy–environment multiple-system coupling in the prefecture-level cities of Jiangsu. From the above formula, we calculated the coupling coordination of the economy–energy, energy–environment, and economy–environment binary systems in 13 prefecture-level cities in Jiangsu, as well as the comprehensive evaluation index, coupling degree, and coupling coordination level of the energy–economy–environment multiple system, as shown in Figure 5.

The above Figure 5a–c depicts the coupling and coordination of the three binary systems of economy–energy, energy–environment, and economy–environment in cities of Jiangsu from 2010 to 2020, respectively. It can be seen from the figure that there was a certain unity and heterogeneity in the dual interactive coupling-coordination system of energy, economy, and environment at the urban level in Jiangsu.

On the whole, the coupling and coordination degree of the economic energy and economic environment dual systems of cities in Jiangsu was relatively low around 2010 and fluctuated in 2013 and 2014, but generally showed an upward trend year by year. Overall, the primary coordinated development was basically achieved, and the coupling-coordination degree of each city was relatively high, which also shows that the green-economy development and sustainable-development strategy achieved certain results.

The coupling and coordination degree of the energy–environment binary system was high at the initial stage, but the regional difference was large. From 2010 to 2020, the growth rate maintained a slow growth trend and regional differences still existed, showing a significant regional heterogeneity.

The coupling and coordination degree of the two binary systems of economy–environment and economy–energy was low at the initial stage, in which economy–environment tended to rise at a uniform rate, and the coupling and coordination degree was high in 2020, which was at the level of high-quality coordinated development. In the early stage of economy–energy development, regional differences were large, but the differences were significantly reduced by 2020.

By 2020, the coefficient of the economy–environment and energy–environment dual coupling-coordination system was about 0.9, which was at the level of high-quality coordinated development. However, the degree of economy–energy coupling coordination was about 0.8, which indicates that we should pay attention to the coupling-development relationship between economy and energy in the future. While promoting the economic development of Jiangsu, we should improve the energy-utilization rate and reform and innovate the production mode of enterprises so as to solve the contradiction between high-quality economic development and energy utilization.

On the basis of calculating the weight of each index, this paper used the linear-weighting method to calculate the comprehensive evaluation index, coupling degree, and coupling-coordination degree of the three subsystems and obtained the average value of each year, as shown in

Table 6. Jiangsu 3E system comprehensive evaluation-index-coupling degree and the mean of coupling coordination.

Year	Economy	Energy	Environment	Index	Coupling	Degree
2010	0.1497	0.6381	0.2740	0.3206	0.5104	0.4004
2011	0.1264	0.5555	0.2634	0.3151	0.7869	0.4946
2012	0.2556	0.5665	0.3044	0.3755	0.8923	0.5769
2013	0.2458	0.4267	0.3149	0.3291	0.9317	0.5523
2014	0.3861	0.4282	0.3378	0.3840	0.9491	0.6019
2015	0.4543	0.4779	0.4922	0.4748	0.9771	0.6769
2016	0.5126	0.3974	0.6370	0.5157	0.9584	0.7008
2017	0.5134	0.4272	0.6068	0.5158	0.9696	0.7052
2018	0.5838	0.4030	0.6246	0.5371	0.9528	0.7127
2019	0.6701	0.3966	0.6217	0.5628	0.9466	0.7281
2020	0.7318	0.4375	0.7290	0.6328	0.9516	0.7724

.

(2)

Temporal evolution of economy–energy–environment coupling degree

The coupling degree reflects the degree of interaction between the two parties and is a measure of the degree of correlation between systems. It can be seen from Table 6 that the 3E coupling coefficient of Jiangsu increased from 0.5 to about 0.9 between 2010 and 2020, reaching a high level in 2012 and remaining in a very stable high-level coupling state, indicating that there was a strong and stable relationship between the energy, economy, and environment systems of Jiangsu. It can be seen from Figure 6 that in 2010, the coupling degree of economy–energy–environment of each city was quite different, among which Yangzhou, Nantong, and Zhenjiang had a high level of coupling degree, whereas Changzhou, Yancheng, and Huai’an had a low level of coupling degree. From 2010 to 2012, the coupling degree showed a rapid growth trend, rising from 0.4 to 0.7. Subsequently, the coupling degree of each city was relatively stable, fluctuating slightly in the range of 0.8~0.9, and the regional difference gradually narrowed.

(3)

Temporal evolution of economy–energy–environment coupling-coordination degree

It can be concluded from Table 6 that the coordination degree of 3E coupling in Jiangsu transitioned from 0.4 in 2010 to 0.7 in 2020, and the development type transitioned from being on the verge of maladjustment and recession to intermediate coordinated development. Among them, the period from 2010 to 2011 was on the verge of maladjustment and recession. From 2011 to 2012, it was in a stage of barely coordinated development. The period from 2013 to 2020 was the stage of intermediate coordinated development. In general, the level of 3E coupling and coordinated development in Jiangsu Province was constantly improving and moving towards well-coordinated development and even high-quality coordinated development, which is due to the continuous promotion of China’s carbon-neutral strategy and sustainable-development strategy.

It can be seen from Figure 7 that the coordination level of economy–energy–environment coupling in cities at all levels in Jiangsu Province showed an overall upward trend from 2010 to 2020. Among them, the growth rate from 2010 to 2012 was relatively large, with an approximate coefficient between 0.2 and 0.6. Subsequently, from 2012 to 2017, it showed a steady upward trend, and the coupling coordination coefficient was between 0.6 and 0.8. After 2017, the coefficient roughly increased from 0.7 to 0.9, and the type of coupling and coordination transitioned from primary coordinated development to well-coordinated development.

At the urban level, there were significant differences in the level of 3E system coupling and coordination among different cities. From 2010 to 2012, Changzhou, Yancheng, and Xuzhou had a low level of coupling coordination, whereas Yangzhou, Wuxi, and Nantong had a high level of coupling coordination; From 2012 to 2017, the coupling coordination level of Xuzhou, Lianyungang, and Suqian was low, whereas that of Yancheng, Yangzhou, and Huai’an was high. After 2017, the coupling-coordination level of Xuzhou, Changzhou, and Zhenjiang was low, whereas that of Nanjing, Suzhou, and Nantong was high.

3.3. Spatial Differences of 3E Coupling and Coordinated Development in Jiangsu

In order to reflect the spatial-evolution characteristics of the coordinated development of urban energy–economy–environment coupling in Jiangsu, this paper adopted the change trend of coupling and coordination of 13 prefecture-level cities in Jiangsu in 2010, 2014, 2017, and 2020. The results are shown in

Table 7. Economy–energy–environment coupling-coordination degree in Jiangsu in 2010, 2014, 2017, and 2020.

Type	City	CCD in 2010	CCD in 2014	CCD in 2017	CCD in 2020
Southern Jiangsu	Nanjing	0.3236	0.6132	0.7691	0.8842
	Suzhou	0.3816	0.5947	0.6730	0.9136
	Wuxi	0.4660	0.5746	0.6423	0.7626
	Changzhou	0.2020	0.5972	0.6336	0.7080
	Zhenjiang	0.4134	0.5427	0.7582	0.7016
Central Jiangsu	Yangzhou	0.6686	0.6680	0.7149	0.7700
	Taizhou	0.4299	0.6423	0.7530	0.7735
	Nantong	0.4511	0.5950	0.7601	0.8650
Northern Jiangsu	Xuzhou	0.3452	0.5325	0.6470	0.6752
	Lianyung	0.4439	0.6331	0.6125	0.7567
	Suqian	0.4184	0.6559	0.7182	0.7047
	Huaian	0.3419	0.6117	0.7584	0.7938
	Yancheng	0.3194	0.5639	0.7268	0.7321

.

In 2010, as a whole, the coupling and coordination coefficient was about 0.3~0.5. The economy–energy–environment coupling and coordination was mainly in the transitional and harmonious stage, and the development of each city was relatively average. In terms of regions, except for Changzhou, the development level of southern and central Jiangsu was generally higher than that of northern Jiangsu. Among them, Yangzhou, Nantong, Wuxi, and other cities had a high level of development, and the overall difference was low; in northern Jiangsu, Lianyungang and Suqian had a high level of development.

In 2014, as a whole, the coupling-coordination coefficient was about 0.5~0.6, which is in the stage of barely coordinated development. In terms of regions, there were some differences at the urban level. Among them, Yangzhou and Taizhou in central Jiangsu Province had a high level of coupling and coordinated development, whereas Suqian in northern Jiangsu had a high level of development of about 0.65. The level of coupling and coordination in southern Jiangsu was generally high, with a coefficient of about 0.6.

In 2017, the overall coupling and coordination level of Jiangsu Province was in the stage of coordinated development, and the coordination level was high in the middle and low areas on both sides. The urban-development coefficient in the middle was 0.7~0.77, and the overall coupling and coordination degree was high. However, the development level of northern Jiangsu, such as Lianyungang, and southern Jiangsu, such as Changzhou and Wuxi, was lower than that of other cities, at about 0.61~0.65, and the development speed was relatively slow.

In 2020, the level of urban coupling and coordination in Jiangsu was basically in a well-coordinated development stage, which was higher than that in 2017. From a regional perspective, the level of coupling and coordination increased from north to south. Among them, the coupling-coordinated dispatching in southern Jiangsu, such as Nanjing, Suzhou, and Nantong, was the highest, with the development coefficient between 0.85 and 0.95, and gradually tended to the stage of high-quality coordinated development. The coupling-coordination level in central Jiangsu was about 0.75~0.85, whereas that in northern Jiangsu was about 0.7~0.75.

To sum up, the level of urban coupling and coordination in Jiangsu showed an upward trend from 2010 to 2020, and the development level increased from northern and central Jiangsu to southern Jiangsu. Among them, Suzhou, Nanjing, and Nantong had a high degree of coupling and coordination. Suzhou and Nantong may have had higher economic development level, reasonable industrial structure, and a higher energy-utilization rate due to the radiation and driving role of Shanghai, and Nanjing has a relatively high proportion of industry. As the provincial capital, its economic development is fast and its ecological construction is relatively perfect. The type of coupling-coordination degree of each city in Jiangsu saw little change, even when the change was only between adjacent types, which also shows that the spatial pattern of the coupling-coordination degree of each city in Jiangsu Province was relatively stable.

4. Conclusions and Suggestions

4.1. Research Conclusion

Based on the theoretical framework of DPSIRM, this paper analyzed the mechanism of energy–economy–environment coupling and coordinated development of 13 prefecture-level cities in Jiangsu Province and constructed an evaluation system of coupling and coordinated development. The entropy method and comprehensive-evaluation method were used to process and analyze the index data from 2010 to 2020, build a coupling-coordination-degree model, calculate the coupling-coordination level of the binary system and the multiple system, and analyze the time-series differences and spatial characteristics. When constructing the coupling-coordination indicator system, the sustainable-development and carbon-neutral strategies were comprehensively considered, and the spatial and temporal heterogeneity of Jiangsu urban agglomeration was analyzed. Finally, the 3E3S strategic model of Jiangsu Province was formed to achieve the coordinated development of energy, economy, and environment under the “double carbon” and sustainable background of Jiangsu and build an effective path for the regional coordinated development of Jiangsu.

The main conclusions of this paper are as follows:

(1) From the perspective of the dual-coupling system, the development level of Jiangsu Province’s economy–energy coupling-coordination system was low at the initial stage and the regional difference was large, until the later stage, when the regional difference was significantly reduced. At the initial stage, the level of the economic–environmental coupling-coordination system was low and the regional difference was small, showing a roughly uniform upward trend. The level of the energy–environment coupling-coordination system was high at the initial stage, with large regional differences, significant regional heterogeneity, and slow growth. Green economy and sustainable development achieved certain results. In the future, attention should be paid to energy utilization and the enterprise-production mode.

(2) From the perspective of the ternary-coupling system, the comprehensive evaluation index of economy and environment in Jiangsu Province was generally on the rise, indicating that the economic-development level of Jiangsu Province rose steadily in the past decade and that the national ecological civilization construction achieved remarkable results. The comprehensive energy-evaluation index showed a general downward trend, indicating a lack of development level. The coupling degree of energy–economy–environment in Jiangsu rose rapidly at the initial stage of the study and then showed a steady and slow growth trend, indicating that the interaction degree and correlation degree among subsystems were in a high-level coupling state—that is, the energy, economy, and environment development in Jiangsu were closely related to each other. The coordination degree of energy–economy–environment coupling in Jiangsu Province was on the rise year by year, and the type of coupling coordination transitioned from being on the verge of maladjustment and recession to intermediate coordinated development. The overall development level was good, and it moved towards high-quality coordinated development. Due to the continuous promotion of China’s carbon-neutral strategy and sustainable-development strategy, as well as the continuous development of green transformation and the energy revolution, the construction of ecological civilization in Jiangsu has been constantly strengthened, which has also promoted the coordinated development of the 3E system in Jiangsu.

(3) From the perspective of space, the overall development of Jiangsu Province was on the rise, and the development level increased from the north to the middle to the south of Jiangsu Province. At the urban level, Nanjing and Nantong had a high degree of coupling and coordination, whereas Lianyungang and Suqian had a low degree of coupling and coordination. The radiation and driving role of the surrounding developed cities, the reasonable industrial structure, and the high energy-utilization rate were mainly considered. The type of coupling-coordination degree of each city was mainly in the transformation of adjacent types, and the spatial pattern of coupling coordination of each city was relatively stable.

4.2. Policy Suggestion

Based on the discussion on the status quo of energy–economy–environment development in Jiangsu and the above results, the following suggestions are put forward:

(1) the strategic-development awareness of energy–economy–environment should be strengthened to achieve the coordinated development strategy of 3E in Jiangsu Province. Based on the concept of multi-system coupling and coordination of energy, economy, and environment, the development plan can be formulated according to the current situation of urban development. While seeking for rapid economic development, it should also be committed to improving the energy utilization and low-carbon development of the industry. For cities with relatively backward economic development, the environment and energy development should be taken as the guidance to promote economic development in a reverse direction, and a carbon-neutral strategy and sustainable-development strategy should be implemented from a coordinated development pace of 3E3S.

(2) Measures should be adjusted to local conditions according to the development level and actual situation of each city. Southern Jiangsu drives the overall economic development and gives full play to its leading and exemplary role. Northern Jiangsu and central Jiangsu actively adjust the economic development mode, optimize the industrial structure and technological structure, reduce the dependence on energy, and develop low-carbon economy and sustainable economy. At the same time, regional cooperation should be strengthened. While formulating policies, local governments should consider neighboring cities to realize complementary advantages.

(3) The government should establish and improve the consultation mechanism between relevant departments so that the relevant management and planning of various departments can be fully connected and enhance scientific and rational decision-making. An early warning mechanism should be established for the coordinated development of energy, economy, and environment, and the dynamics of unbalanced cities should be detected, optimized, and adjusted.

4.3. Limitation

(1) When building the coupling-coordination-degree model of energy–economy–environment, the weight of the three systems is averaged. However, in the actual situation, it should be considered that the weight ratios of the three systems are different in different cities and different development stages. In the future, the weight should be considered, and methods such as parameter simulation and scenario assumption should be constructed. (2) In the spatial-empirical analysis, only the regional coupling-coordination degree is compared and analyzed, but not much consideration is given to the spatial-autocorrelation effect or spatial-spillover effect. A spatial-empirical model should be built to analyze and improve, and the state and trend of regional spatial heterogeneity should be explored more deeply. (3) When building the 3E3S strategic model and indicator integration, we mainly focus on energy–economy–environment (3E), while sustainable development and carbon neutralization are the development background and assistance. In the future, we should deeply explore the construction of 3S indicators and improve the 3S indicator system.